Audrain-McGovern Janet, Rodriguez Daniel, Epstein Leonard H, Cuevas Jocelyn, Rodgers Kelli, Wileyto E Paul
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
Drug Alcohol Depend. 2009 Aug 1;103(3):99-106. doi: 10.1016/j.drugalcdep.2008.12.019. Epub 2009 May 14.
Although higher delay discounting rates have been linked to cigarette smoking, little is known about the stability of delay discounting, whether delay discounting promotes smoking acquisition, whether smoking contributes to impulsive choices, or if different relationships exist in distinct subgroups. This study sought to fill these gaps within a prospective longitudinal cohort study (N=947) spanning mid-adolescence to young adulthood (age 15-21 years old). Smoking and delay discounting were measured across time. Covariates included peer and household smoking, academic performance, depression, novelty seeking, inattention and hyperactivity/impulsivity symptoms, and alcohol and marijuana use. The associated processes latent growth curve modeling (LGCM) with paths from the delay discounting level factor (baseline measure) and the trend factor (slope) to the smoking trend factor (slope) fit the data well, chi(2)((19,n=947)) =15.37, p=.70, CFI=1.00, RMSEA=0, WRMR=.36. The results revealed that delay discounting did not change significantly across time. Baseline delay discounting had a significant positive effect on smoking trend (beta=.08, z=2.16, p=.03). A standard deviation (SD=1.41) increase in baseline delay discounting resulted in an 11% increase (OR=1.11, 95% CI=1.03, 1.23) in the odds of smoking uptake. The alternative path LCGM revealed that smoking did not significantly impact delay discounting (p's>.05). Growth mixture modeling identified three smoking trajectories: nonsmokers, early/fast smoking adopters, and slow smoking progressors. Delay discounting was higher in the smoking versus nonsmoking trajectories, but did not discriminate between the smoking trajectories, despite different acquisition patterns. Delay discounting may provide a variable by which to screen for smoking vulnerability and help identify subgroups to target for more intensive smoking prevention efforts that include novel behavioral components directed toward aspects of impulsivity.
尽管较高的延迟折扣率与吸烟有关，但对于延迟折扣的稳定性、延迟折扣是否促进吸烟行为的养成、吸烟是否导致冲动选择，或者在不同亚组中是否存在不同关系，人们了解甚少。本研究旨在通过一项前瞻性纵向队列研究（N = 947）填补这些空白，该研究涵盖了从青春期中期到青年期（15 - 21岁）的人群。对吸烟和延迟折扣进行了跨时间测量。协变量包括同伴和家庭吸烟情况、学业成绩、抑郁、寻求新奇、注意力不集中和多动/冲动症状，以及酒精和大麻使用情况。关联过程潜在增长曲线模型（LGCM），其路径从延迟折扣水平因子（基线测量）和趋势因子（斜率）到吸烟趋势因子（斜率），与数据拟合良好，卡方值（(19,n = 947)）= 15.37，p = 0.70，CFI = 1.00，RMSEA = 0，WRMR = 0.36。结果显示，延迟折扣随时间没有显著变化。基线延迟折扣对吸烟趋势有显著的正向影响（β = 0.08，z = 2.16，p = 0.03）。基线延迟折扣增加一个标准差（SD = 1.41）会导致吸烟起始几率增加11%（OR = 1.11，95%置信区间 = 1.03，1.23）。替代路径LGCM显示，吸烟对延迟折扣没有显著影响（p值>0.05）。增长混合模型确定了三种吸烟轨迹：不吸烟者、早期/快速吸烟者和缓慢吸烟者。吸烟轨迹中的延迟折扣高于非吸烟轨迹，但尽管吸烟模式不同，延迟折扣在吸烟轨迹之间并无区分作用。延迟折扣可能提供了一个变量，通过它可以筛查吸烟易感性，并有助于识别需要针对更强化的吸烟预防努力的亚组，这些努力包括针对冲动性方面的新颖行为成分。
